126 research outputs found
Physiological-based Driver Monitoring Systems: A Scoping Review
A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PD
An Empirical study on Predicting Blood Pressure using Classification and Regression Trees
Blood pressure diseases have become one of the major threats to human health. Continuous measurement of bloodpressure has proven to be a prerequisite for effective incident prevention. In contrast with the traditional prediction models with lowmeasurement accuracy or long training time, non-invasive blood pressure measurement is a promising use for continuousmeasurement. Thus in this paper, classification and regression trees (CART) are proposed and applied to tackle the problem. Firstly,according to the characteristics of different information, different CART models are constructed. Secondly, in order to avoid theover-fitting problem of these models, the cross-validation method is used for selecting the optimum parameters so as to achieve thebest generalization of these models. Based on the biological data collected from CM400 monitor, this approach has achieved betterperformance than the common existing models such as linear regression, ridge regression, the support vector machine and neuralnetwork in terms of accuracy rate, root mean square error, deviation rate, Theil IC, and the required training time is also comparativelyless. With increasing data, the accuracy rate of predicting systolic blood pressure and diastolic blood pressure by CART exceeds 90%,and the training time is less than 0.5s
Efficient Personalized Learning for Wearable Health Applications using HyperDimensional Computing
Health monitoring applications increasingly rely on machine learning
techniques to learn end-user physiological and behavioral patterns in everyday
settings. Considering the significant role of wearable devices in monitoring
human body parameters, on-device learning can be utilized to build personalized
models for behavioral and physiological patterns, and provide data privacy for
users at the same time. However, resource constraints on most of these wearable
devices prevent the ability to perform online learning on them. To address this
issue, it is required to rethink the machine learning models from the
algorithmic perspective to be suitable to run on wearable devices.
Hyperdimensional computing (HDC) offers a well-suited on-device learning
solution for resource-constrained devices and provides support for
privacy-preserving personalization. Our HDC-based method offers flexibility,
high efficiency, resilience, and performance while enabling on-device
personalization and privacy protection. We evaluate the efficacy of our
approach using three case studies and show that our system improves the energy
efficiency of training by up to compared with the state-of-the-art
Deep Neural Network (DNN) algorithms while offering a comparable accuracy
Physiological Approach To Characterize Drowsiness In Simulated Flight Operations During Window Of Circadian Low
Drowsiness is a psycho-physiological transition from awake towards falling sleep and its detection is crucial in aviation industries. It is a common cause for pilot’s error due to unpredictable work hours, longer flight periods, circadian disruption, and insufficient sleep. The pilots’ are prone towards higher level of drowsiness during window of circadian low (2:00 am- 6:00 am). Airplanes require complex operations and lack of alertness increases accidents. Aviation accidents are much disastrous and early drowsiness detection helps to reduce such accidents. This thesis studied physiological signals during drowsiness from 18 commercially-rated pilots in flight simulator. The major aim of the study was to observe the feasibility of physiological signals to predict drowsiness. In chapter 3, the spectral behavior of electroencephalogram (EEG) was studied via power spectral density and coherence. The delta power reduced and alpha power increased significantly (
Machine Learning Models for Mental Stress Classification based on Multimodal Biosignal Input
Mental stress is a largely prevalent condition directly or indirectly responsible for
almost half of all work-related diseases. Work-Related Stress is the second most impactful
occupational health problem in Europe, behind musculoskeletal diseases. When mental
health is adequately handled, a worker’s well-being, performance, and productivity can
be considerably improved.
This thesis presents machine learning models to classify mental stress experienced by
computer users using physiological signals including heart rate, acquired using a smart-
watch; respiration, derived from a smartphone’s acc placed on the chest; and trapezius
electromyography, using proprietary electromyography sensors. Two interactive proto-
cols were implemented to collect data from 12 individuals. Time and frequency domain
features were extracted from the heart rate and electromyography signals, and statistical
and temporal features were extracted from the derived respiration signal.
Three algorithms: Support Vector Machine, Random Forest, and K-Nearest-Neighbor
were employed for mental stress classification. Different input modalities were tested
for the machine learning models: one for each physiological signal and a multimodal
one, combining all of them. Random Forest obtained the best mean accuracy (98.5%) for
the respiration model whereas K-Nearest-Neighbor attained higher mean accuracies for
the heart rate (89.0%) left, right and total electromyography (98.9%, 99.2%, and 99.3%)
models. KNN algorithm was also able to achieve 100% mean accuracy for the multimodal
model. A possible future approach would be to validate these models in real-time.O stress mental é uma condição amplamente prevalente direta ou indiretamente
responsável por quase metade de todas doenças relacionadas com trabalho. O stress expe-
rienciado no trabalho é o segundo problema de saúde ocupacional com maior impacto na
Europa, depois das doenças músculo-esqueléticas. Quando a saúde mental é adequada-
mente cuidada, o bem-estar, o desempenho e a produtividade de um trabalhador podem
ser consideravelmente melhorados.
Esta tese apresenta modelos de aprendizagem automática que classificam o stress
mental experienciado por utilizadores de computadores recorrendo a sinais fisiológi-
cos, incluindo a frequência cardíaca, adquirida pelo sensor de fotopletismografia de um
smartwatch; a respiração, derivada de um acelerómetro incorporado no smartphone po-
sicionado no peito; e electromiografia de cada um dos músculos trapézios, utilizando
sensores electromiográficos proprietários. Foram implementados dois protocolos inte-
ractivos para recolha de dados de 12 indivíduos. Características do domínio temporal
e de frequência foram extraídas dos sinais de frequência cardíaca e electromiografia, e
características estatísticas e temporais foram extraídas do sinal respiratório.
Três algoritmos entitulados K-Nearest-Neighbor, Random Forest, e Support Vector
Machine foram utilizados para a classificação do stress mental. Foram testadas diferentes
modalidades de dados para os modelos de aprendizagem automática: uma para cada sinal
fisiológico e uma multimodal, combinando os três. O Random Forest obteve a melhor
precisão média (98,5%) para o modelo de respiração enquanto que o K-Nearest-Neighbor
atingiu uma maior precisão média nos modelos de frequência cardíaca (89,0%) e electro-
miografia esquerda, direita e total (98,9%, 99,2%, e 99,3%). O algoritmo KNN conseguiu
ainda atingir uma precisão média de 100% para o modelo multimodal. Uma possível
abordagem futura seria efetuar uma validação destes modelos em tempo real
Fatigue Detection for Ship OOWs Based on Input Data Features, from The Perspective of Comparison with Vehicle Drivers: A Review
Ninety percent of the world’s cargo is transported by sea, and the fatigue of ship officers of the watch (OOWs) contributes significantly to maritime accidents. The fatigue detection of ship OOWs is more difficult than that of vehicles drivers owing to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analysed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organised based on input sources, which include the physiological/behavioural features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarised. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analysed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a non-negligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behaviour videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realise quantitative, accurate, and real-time fatigue detections in OOWs on actual ships
Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection
Drowsy driving is a major cause of road accidents, but drivers are dismissive
of the impact that fatigue can have on their reaction times. To detect
drowsiness before any impairment occurs, a promising strategy is using Machine
Learning (ML) to monitor Heart Rate Variability (HRV) signals. This work
presents multiple experiments with different HRV time windows and ML models, a
feature impact analysis using Shapley Additive Explanations (SHAP), and an
adversarial robustness analysis to assess their reliability when processing
faulty input data and perturbed HRV signals. The most reliable model was
Extreme Gradient Boosting (XGB) and the optimal time window had between 120 and
150 seconds. Furthermore, SHAP enabled the selection of the 18 most impactful
features and the training of new smaller models that achieved a performance as
good as the initial ones. Despite the susceptibility of all models to
adversarial attacks, adversarial training enabled them to preserve
significantly higher results, especially XGB. Therefore, ML models can
significantly benefit from realistic adversarial training to provide a more
robust driver drowsiness detection.Comment: 10 pages, 2 tables, 3 figures, AIME 2023 conferenc
A model for inebriation recognition in humans using computer vision
Abstract: Inebriation is a situational impairment caused by the consumption of alcohol affecting the consumer's interaction with the environment around them...M.Sc. (Information Technology
Review of Ethanol Intoxication Sensing Technologies and Techniques
The field of alcohol intoxication sensing is over 100 years old, spanning the fields of medicine, chemistry, and computer science, aiming to produce the most effective and accurate methods of quantifying intoxication levels. This review presents the development and the current state of alcohol intoxication quantifying devices and techniques, separated into six major categories: estimates, breath alcohol devices, bodily fluid testing, transdermal sensors, mathematical algorithms, and optical techniques. Each of these categories was researched by analyzing their respective performances and drawbacks. We found that the major developments in monitoring ethanol intoxication levels aim at noninvasive transdermal/optical methods for personal monitoring. Many of the “categories” of ethanol intoxication systems overlap with each other with to a varying extent, hence the division of categories is based only on the principal operation of the techniques described in this review. In summary, the gold-standard method for measuring blood ethanol levels is through gas chromatography. Early estimation methods based on mathematical equations are largely popular in forensic fields. Breath alcohol devices are the most common type of alcohol sensors on the market and are generally implemented in law enforcement. Transdermal sensors vary largely in their sensing methodologies, but they mostly follow the principle of electrical sensing or enzymatic reaction rate. Optical devices and methodologies perform well, with some cases outperforming breath alcohol devices in terms of the precision of measurement. Other estimation algorithms consider multimodal approaches and should not be considered alcohol sensing devices, but rather as prospective measurement of the intoxication influence. This review found 38 unique technologies and techniques for measuring alcohol intoxication, which is testament to the acute interest in the innovation of noninvasive technologies for assessing intoxication
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